system

Abstract

The system according to this embodiment aims to enable automatic participation at the start time of a meeting. [Solution] The system according to the embodiment comprises a collection unit, a notification unit, and a participation unit. The collection unit synchronizes with the user's calendar. The notification unit displays a notification based on the meeting start time acquired by the collection unit. The participation unit automatically joins the meeting based on the reminder displayed by the notification unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional technologies have problems such as being late for the start time of a meeting, searching for a meeting link, and taking time to participate.

[0005] The system according to an embodiment aims to enable automatic participation in a meeting at the start time of the meeting.

Means for Solving the Problems

[0006] The system according to an embodiment includes a collection unit, a notification unit, and a participation unit. The collection unit synchronizes with the user's calendar. The notification unit displays a notification based on the start time of the meeting acquired by the collection unit. The participation unit automatically participates in the meeting based on the reminder displayed by the notification unit.

Effects of the Invention

[0007] The system according to this embodiment can be configured to automatically join a meeting at the start time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The meeting participation system according to an embodiment of the present invention is a system that improves work efficiency by reducing the time spent preparing for and attending meetings, by allowing participants to automatically join online meetings at the start time of the meeting. The meeting participation system synchronizes with the user's calendar and automatically displays notifications as the meeting start time approaches. For example, reminders pop up 10 minutes, 5 minutes, and 1 minute before the meeting to inform the user of the start of the meeting. After the reminder is displayed, the user can seamlessly join the meeting without clicking on a meeting link. For example, when the reminder is displayed, the user can automatically join the online meeting without any action required from the user. This mechanism reduces the time spent preparing for and attending meetings, thereby improving work efficiency. Furthermore, this mechanism improves meeting productivity. By avoiding the time spent manually searching for meeting links and technical problems, the time from the start of the meeting to getting to the main topic can be shortened. For example, meetings can start smoothly without being late for the scheduled start time. In addition, attendance and participation rates improve. Reminders and automatic participation functions reduce missed meetings and lateness, allowing all relevant parties to participate in all important meetings in a timely manner. This improves the quality of discussions and the speed of decision-making. The system automates meeting preparation time (checking links, logging into meeting tools, setting up cameras and microphones, etc.), eliminating the need for manual operation each time. For example, it can save 5-10 minutes per meeting, and companies that hold multiple meetings per week can expect to save hundreds of hours annually. As a result, meeting participation systems can reduce the time spent on meeting preparation and attendance, thereby improving work efficiency.

[0029] The meeting participation system according to the embodiment comprises a collection unit, a notification unit, and a participation unit. The collection unit synchronizes with the user's calendar. The collection unit can, for example, synchronize with a calendar. The collection unit can select an appropriate synchronization method depending on the type of calendar. The notification unit displays a notification based on the meeting start time acquired by the collection unit. The notification unit can display notifications by methods such as pop-up notifications, email notifications, and push notifications. As the meeting start time approaches, the notification unit can display reminders 10 minutes, 5 minutes, and 1 minute before. For example, a pop-up notification is displayed on the user's desktop screen. An email notification is sent to the user's email address. A push notification is displayed on the user's smartphone. The participation unit automatically joins the meeting based on the reminder displayed by the notification unit. The participation unit can, for example, automatically launch the meeting tool and join the meeting without clicking a meeting link. The participation unit can perform actions such as automatically launching the meeting tool, logging in, and setting up the camera and microphone. For example, the participation unit acquires the meeting link and automatically launches the meeting tool. When the meeting tool is launched, the participant automatically configures the user's camera and microphone. This allows the meeting participation system according to the embodiment to enable users to join meetings smoothly, thereby improving work efficiency.

[0030] The data collection unit synchronizes with the user's calendar. For example, the data collection unit can synchronize with a calendar. This allows the data collection unit to accurately retrieve meeting information registered in the user's calendar and manage it within the system. Furthermore, the data collection unit can select the appropriate synchronization method depending on the type of calendar. For example, it can synchronize with proprietary calendar systems used within a company using APIs or database connections. This allows the data collection unit to support diverse calendar systems and centrally manage user meeting information. Additionally, the data collection unit can periodically update calendar information, reflecting newly added or modified meeting information in real time. This ensures users always have access to the latest meeting information, enabling smooth meeting participation.

[0031] The notification unit displays notifications based on the meeting start time acquired by the data collection unit. Specifically, as the meeting start time approaches, reminders can be displayed 10 minutes, 5 minutes, and 1 minute beforehand. Pop-up notifications appear on the user's desktop screen, visually informing them of the meeting's start. Email notifications are sent to the user's email address and can be checked through their email client. Push notifications appear on the user's smartphone, informing them of the meeting's start via their mobile device. This ensures that users do not miss the start of a meeting, regardless of the device they are using. Furthermore, the notification unit can customize notification methods according to user preferences. For example, only pop-up notifications can be displayed for certain meetings, while email and push notifications can be used in combination for other meetings. The notification unit can also adjust the frequency and timing of notifications according to the importance of the meeting. For example, reminders can be displayed 30 minutes before the start of important meetings and 10 minutes before the start of regular meetings. This allows the notification unit to provide flexible notifications tailored to user needs and ensure that users are notified of the start of meetings.

[0032] The participant unit automatically joins the meeting based on a reminder displayed by the notification unit. Specifically, it can automatically launch the meeting tool and join the meeting without clicking a meeting link. The participant unit obtains the meeting link and automatically launches the meeting tool. For example, it can support a meeting tool, analyze the meeting link, select the appropriate tool, and launch it. Once the meeting tool is launched, the participant unit automatically configures the user's camera and microphone. Specifically, it adjusts the camera resolution and microphone volume to optimal levels to allow the user to join the meeting smoothly. The participant unit can also save the user's login information in advance and automatically log in to the meeting tool. This eliminates the need for the user to enter login information. Furthermore, the participant unit can not only automatically join the meeting at the start time but also automatically leave the meeting at the end time. This allows the user to concentrate on their work without being aware of the start and end of meetings. The participant unit manages the meeting schedule based on the user's calendar information and can join the meeting at the appropriate time. As a result, the meeting participation system according to this embodiment allows users to join meetings smoothly and improves work efficiency.

[0033] The Preparation Department is a department dedicated to reducing meeting preparation time. The Preparation Department can automate preparation tasks such as verifying meeting links, logging into meeting tools, and setting up cameras and microphones. The Preparation Department can automate preparation tasks through methods such as executing scripts or using APIs. The Preparation Department can automatically verify meeting links and log into meeting tools. For example, the Preparation Department can execute a script to obtain meeting links and automatically log into meeting tools. Furthermore, the Preparation Department can automatically configure camera and microphone settings. For example, the Preparation Department can utilize APIs to automatically configure camera and microphone settings. This allows the Preparation Department to reduce meeting preparation time and improve operational efficiency. Some or all of the above processes in the Preparation Department may be performed using AI, or not. For example, the Preparation Department can have AI perform tasks such as verifying meeting links, logging into meeting tools, and configuring cameras and microphones.

[0034] The preparation team can automate preparatory tasks such as verifying meeting links, logging into meeting tools, and setting up cameras and microphones. For example, the preparation team can automate verifying meeting links. The preparation team can automatically obtain meeting links and notify users. For example, the preparation team can execute a script to obtain meeting links and notify users of the obtained links. The preparation team can also automate logging into meeting tools. The preparation team can execute a script to automatically log into meeting tools. For example, the preparation team can obtain login information for meeting tools and execute a script to automatically log in. The preparation team can also automate setting up cameras and microphones. The preparation team can utilize APIs to automatically set up cameras and microphones. For example, the preparation team can use APIs to automatically set up cameras and microphones and complete the setup. This automates the preparatory work for meetings, eliminating the need for manual operation. Some or all of the above processes in the preparation team may be performed using AI, for example, or not. For example, the preparation team can have AI perform tasks such as verifying meeting links, logging into meeting tools, and setting up cameras and microphones.

[0035] The Productivity Improvement Department is a department dedicated to improving the productivity of meetings. For example, the Productivity Improvement Department can shorten the time from the start of a meeting to getting to the main topic by avoiding the time spent manually searching for meeting links and technical problems. The Productivity Improvement Department can improve meeting productivity through methods such as pre-sharing of agendas and timekeeping. The Productivity Improvement Department can share meeting agendas in advance, allowing participants to understand the meeting content beforehand. For example, the Productivity Improvement Department can share meeting agendas via email in advance. Furthermore, the Productivity Improvement Department can ensure smooth meeting progress by performing timekeeping. For example, the Productivity Improvement Department can assign a timekeeper to manage the meeting's progress and ensure smooth meeting flow. This allows the Productivity Improvement Department to improve meeting productivity and increase operational efficiency. Some or all of the above processes in the Productivity Improvement Department may be performed using AI, or not. For example, the Productivity Improvement Department could have AI perform pre-sharing of agendas and timekeeping.

[0036] The Productivity Improvement Department can shorten the time from the start of a meeting to getting to the main topic by avoiding the time spent manually searching for meeting links and technical problems. For example, the Productivity Improvement Department can share meeting links in advance, reducing the time participants spend searching for them. The Productivity Improvement Department can share meeting links via email in advance. The Productivity Improvement Department can also perform technical checks in advance to avoid technical problems. The Productivity Improvement Department can perform technical checks before meetings to prevent technical problems from occurring. For example, the Productivity Improvement Department can check the operation of cameras and microphones before meetings. The Productivity Improvement Department can also ensure smooth meeting progress to shorten the time from the start of a meeting to getting to the main topic. The Productivity Improvement Department can ensure smooth meeting progress by assigning a timekeeper to manage the meeting progress. For example, the Productivity Improvement Department can ensure smooth meeting progress by assigning a timekeeper to manage the meeting progress. In this way, the Productivity Improvement Department can improve meeting productivity and streamline operations. Some or all of the above processes in the Productivity Improvement Department may be performed using AI, for example, or not using AI. For example, the productivity improvement department can have AI perform tasks such as pre-sharing meeting links, technical checks, and timekeeping.

[0037] The Attendance Improvement Department is a department dedicated to improving attendance and participation rates. For example, the Attendance Improvement Department can reduce missed meetings and lateness through features such as reminders and automatic join functions. The Attendance Improvement Department can set reminders and notify users as the meeting start time approaches. For example, it can display reminders 10 minutes, 5 minutes, and 1 minute before the meeting. Furthermore, the Attendance Improvement Department can provide an automatic join function, allowing users to automatically join meetings without clicking a meeting link. For example, it can retrieve the meeting link, automatically launch the meeting tool, and join the meeting. This allows the Attendance Improvement Department to reduce missed meetings and lateness, thereby improving attendance and participation rates. Some or all of the above processes in the Attendance Improvement Department may be performed using AI, or not. For example, the Attendance Improvement Department can delegate the setting of reminders and the execution of the automatic join function to AI.

[0038] The attendance rate improvement unit can reduce missed meetings and lateness through reminders and automatic join functions. For example, the attendance rate improvement unit can set reminders and notify users as the meeting start time approaches. The attendance rate improvement unit can display reminders 10 minutes, 5 minutes, and 1 minute before the meeting. In addition, the attendance rate improvement unit can provide an automatic join function, allowing users to automatically join meetings without clicking a meeting link. For example, the attendance rate improvement unit can obtain the meeting link, automatically launch the meeting tool, and join the meeting. This reduces missed meetings and lateness, improving meeting attendance rates. Some or all of the above processes in the attendance rate improvement unit may be performed using AI, for example, or not. For example, the attendance rate improvement unit can entrust the setting of reminders and the execution of the automatic join function to AI.

[0039] The data collection unit can analyze the user's past meeting participation history and select the optimal synchronization method. For example, the data collection unit can prioritize the synchronization of meetings that the user has frequently attended in the past. The data collection unit can analyze the user's past meeting participation history and prioritize the synchronization of meetings that the user has frequently attended. It can also exclude meetings that the user has not attended in the past from the synchronization. The data collection unit can analyze the user's past meeting participation history and exclude meetings that the user has not attended from the synchronization. Furthermore, the data collection unit can concentrate the synchronization on specific time periods based on the user's past participation history. The data collection unit can analyze the user's past meeting participation history and concentrate the synchronization on specific time periods. This allows the data collection unit to select the optimal synchronization method based on the user's past meeting participation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can entrust the analysis of the user's past meeting participation history to AI.

[0040] The data collection unit can filter the calendar based on the user's current projects and areas of interest during synchronization. For example, the data collection unit can synchronize only meetings related to the user's current projects. The data collection unit can prioritize the synchronization of meetings related to the user's current projects. The data collection unit can also prioritize the synchronization of relevant meetings based on the user's areas of interest. The data collection unit can also prioritize the synchronization of relevant meetings based on the user's areas of interest. Furthermore, the data collection unit can exclude meetings in areas that the user has not expressed interest in during synchronization. The data collection unit can exclude meetings in areas that the user has not expressed interest in during synchronization. This allows the data collection unit to synchronize the calendar based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can leave the filtering of the user's projects and areas of interest to AI.

[0041] The data collection unit can prioritize the synchronization of highly relevant events when synchronizing the calendar, taking into account the user's geographical location. For example, the data collection unit can prioritize the synchronization of meetings held near the user's current location. The data collection unit can prioritize the synchronization of meetings held near the user's current location, taking into account the user's geographical location. Furthermore, if the user is on the move, the data collection unit can prioritize the synchronization of meetings held at their destination. The data collection unit can prioritize the synchronization of meetings held at their destination, taking into account the user's geographical location. In addition, if the user is in a specific region, the data collection unit can prioritize the synchronization of events related to that region. The data collection unit can prioritize the synchronization of events related to that region, taking into account the user's geographical location. This allows the data collection unit to prioritize the synchronization of highly relevant events based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can leave the consideration of the user's geographical location to AI.

[0042] The data collection unit can analyze the user's social media activity and synchronize relevant events when synchronizing the calendar. For example, the data collection unit can prioritize synchronizing events that the user has shown interest in on social media. The data collection unit can analyze the user's social media activity and prioritize synchronizing events that the user has shown interest in. The data collection unit can also prioritize synchronizing events that the user's social media friends are participating in. The data collection unit can analyze the user's social media activity and prioritize synchronizing events that the user's friends are participating in. Furthermore, the data collection unit can prioritize synchronizing events that the user is following on social media. The data collection unit can analyze the user's social media activity and prioritize synchronizing events that the user is following. This allows the data collection unit to synchronize relevant events based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can entrust the analysis of the user's social media activity to AI.

[0043] The notification unit can adjust the level of detail of a notification based on the importance of the meeting. For example, the notification unit can display a detailed notification for important meetings. The notification unit can display a detailed notification based on the importance of the meeting. The notification unit can also display a concise notification for less important meetings. The notification unit can display a concise notification based on the importance of the meeting. Furthermore, the notification unit can customize the content of the notification according to the importance of the meeting. The notification unit can customize the content of the notification according to the importance of the meeting. This allows the notification unit to adjust the level of detail of the notification according to the importance of the meeting. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can entrust the adjustment of the level of detail of the notification based on the importance of the meeting to AI.

[0044] The notification unit can apply different notification algorithms depending on the meeting category when issuing notifications. For example, the notification unit can display a formal notification for business meetings. The notification unit can display formal notifications depending on the meeting category. The notification unit can also display relaxed notifications for casual meetings. The notification unit can display relaxed notifications depending on the meeting category. Furthermore, the notification unit can display quick and prominent notifications for urgent meetings. The notification unit can display quick and prominent notifications depending on the meeting category. This allows the notification unit to apply different notification algorithms depending on the meeting category. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can entrust the application of notification algorithms according to the meeting category to AI.

[0045] The notification unit can determine the priority of notifications based on the meeting start time. For example, the notification unit can display notifications preferentially when the meeting start time is approaching. The notification unit can display notifications preferentially when the meeting start time is approaching. The notification unit can also lower the priority of notifications when the meeting start time is far away. The notification unit can lower the priority of notifications when the meeting start time is far away. Furthermore, the notification unit can customize the content of notifications according to the meeting start time. The notification unit can customize the content of notifications based on the meeting start time. This allows the notification unit to determine the priority of notifications based on the meeting start time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can leave the determination of notification priority based on the meeting start time to AI.

[0046] The notification unit can adjust the order of notifications based on the relevance of the meetings when it sends notifications. For example, the notification unit can prioritize notifications for meetings that are important to the user. The notification unit can prioritize notifications for meetings that are important to the user based on the relevance of the meetings. The notification unit can also postpone notifications for meetings that are less relevant to the user based on the relevance of the meetings. Furthermore, the notification unit can customize the order of notifications according to the relevance of the meetings. The notification unit can customize the order of notifications based on the relevance of the meetings. This allows the notification unit to adjust the order of notifications based on the relevance of the meetings. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can entrust the adjustment of the order of notifications based on the relevance of the meetings to AI.

[0047] The participation unit can analyze the user's past participation history and select the optimal participation method during automatic participation. For example, the participation unit can suggest the optimal participation method based on the participation methods the user has used in the past. The participation unit can analyze the user's past participation history and suggest the optimal participation method based on the participation methods used in the past. Furthermore, the participation unit can select the most efficient participation method from the user's past participation history. The participation unit can analyze the user's past participation history and select the most efficient participation method. In addition, the participation unit can analyze the user's past participation history and suggest the optimal means of participation. The participation unit can analyze the user's past participation history and suggest the optimal means of participation. As a result, the participation unit can select the optimal participation method based on the user's past participation history. Some or all of the above processing in the participation unit may be performed using AI, for example, or without AI. For example, the participation unit can entrust the analysis of the user's past participation history to AI.

[0048] The participation unit can customize the means of participation based on the user's current living situation when automatically joining. For example, if the user is at home, the participation unit can provide a means of participation for home use. The participation unit can provide a means of participation for home use if the user is at home, based on the user's current living situation. Furthermore, if the user is out, the participation unit can provide a means of participation for office use if the user is at the office, based on the user's current living situation. In this way, the participation unit can customize the means of participation based on the user's current living situation. Some or all of the above processing in the participation unit may be performed using AI, for example, or not using AI. For example, the participation unit can leave the customization of the means of participation based on the user's living situation to AI.

[0049] The participant function can select the optimal participation method by considering the user's geographical location when automatically joining a meeting. For example, the participant function can automatically prioritize joining meetings held near the user's current location. The participant function can automatically prioritize joining meetings held near the user's current location by considering the user's geographical location. Furthermore, if the user is traveling, the participant function can automatically join meetings held at their destination. The participant function can automatically join meetings held at their destination if the user is traveling, considering the user's geographical location. In addition, if the user is in a specific region, the participant function can automatically join meetings related to that region. The participant function can automatically join meetings related to that region if the user is in a specific region by considering the user's geographical location. This allows the participant function to select the optimal participation method based on the user's geographical location. Some or all of the above processing in the participant function may be performed using AI, for example, or without AI. For example, the participant function can leave the consideration of the user's geographical location to AI.

[0050] The participation unit can analyze the user's social media activity and suggest participation methods when automatically joining. For example, the participation unit can automatically join meetings that the user has shown interest in on social media. The participation unit can analyze the user's social media activity and automatically join meetings that the user has shown interest in. Furthermore, the participation unit can automatically join meetings that the user's social media friends are attending. The participation unit can analyze the user's social media activity and automatically join meetings that friends are attending. In addition, the participation unit can automatically join meetings of groups that the user follows on social media. The participation unit can analyze the user's social media activity and automatically join meetings of groups that the user follows. This allows the participation unit to suggest the most suitable participation method based on the user's social media activity. Some or all of the above processing in the participation unit may be performed using AI, for example, or not. For example, the participation unit can entrust the analysis of the user's social media activity to AI.

[0051] The preparation unit can analyze the user's past preparation history and select the optimal preparation method during the preparation process. For example, the preparation unit can propose the optimal preparation method based on the preparation methods the user has used in the past. The preparation unit can analyze the user's past preparation history and propose the optimal preparation method based on the methods used in the past. Furthermore, the preparation unit can select the most efficient preparation method from the user's past preparation history. The preparation unit can analyze the user's past preparation history and select the most efficient preparation method. In addition, the preparation unit can analyze the user's past preparation history and propose the optimal preparation means. This allows the preparation unit to select the optimal preparation method based on the user's past preparation history. Some or all of the above processing in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation unit can entrust the analysis of the user's past preparation history to AI.

[0052] The preparation unit can select the optimal preparation method during the preparation process, taking into account the user's device information. For example, if the user is using a smartphone, the preparation unit can provide a preparation method optimized for smartphones. The preparation unit can consider the user's device information and provide a preparation method optimized for smartphones if the user is using a smartphone. Furthermore, if the user is using a tablet, the preparation unit can provide a preparation method optimized for tablets if the user is using a tablet. In addition, if the user is using a desktop, the preparation unit can provide a preparation method optimized for desktops if the user is using a desktop. The preparation unit can consider the user's device information and provide a preparation method optimized for desktops if the user is using a desktop. This allows the preparation unit to select the optimal preparation method based on the user's device information. Some or all of the above processing in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation unit can leave the consideration of the user's device information to AI.

[0053] The productivity improvement unit can analyze the user's past meeting history and select the optimal productivity improvement method when improving productivity. For example, the productivity improvement unit can propose the optimal method based on the productivity improvement methods the user has used in the past. The productivity improvement unit can analyze the user's past meeting history and propose the optimal method based on the methods used in the past. Furthermore, the productivity improvement unit can select the most efficient productivity improvement method from the user's past meeting history. The productivity improvement unit can analyze the user's past meeting history and select the most efficient productivity improvement method. In addition, the productivity improvement unit can analyze the user's past meeting history and propose the optimal means of productivity improvement. This allows the productivity improvement unit to select the optimal productivity improvement method based on the user's past meeting history. Some or all of the above processing in the productivity improvement unit may be performed using AI, for example, or without AI. For example, the productivity improvement unit can entrust the analysis of the user's past meeting history to AI.

[0054] The productivity improvement unit can select the optimal productivity improvement method when improving productivity, taking into account the user's device information. For example, if the user is using a smartphone, the productivity improvement unit can provide a productivity improvement method optimized for smartphones. The productivity improvement unit can consider the user's device information and provide a productivity improvement method optimized for smartphones if the user is using a smartphone. Furthermore, if the user is using a tablet, the productivity improvement unit can provide a productivity improvement method optimized for tablets if the user is using a tablet. The productivity improvement unit can consider the user's device information and provide a productivity improvement method optimized for tablets if the user is using a tablet. In addition, if the user is using a desktop, the productivity improvement unit can provide a productivity improvement method optimized for desktops if the user is using a desktop. The productivity improvement unit can consider the user's device information and provide a productivity improvement method optimized for desktops if the user is using a desktop. This allows the productivity improvement unit to select the optimal productivity improvement method based on the user's device information. Some or all of the above processing in the productivity improvement unit may be performed using AI, for example, or without AI. For example, the productivity improvement unit can leave the consideration of the user's device information to AI.

[0055] The attendance rate improvement unit can analyze the user's past attendance history to select the optimal attendance rate improvement method when improving attendance rates. For example, the attendance rate improvement unit can propose the optimal method based on the attendance rate improvement methods the user has used in the past. The attendance rate improvement unit can analyze the user's past attendance history and propose the optimal method based on the attendance rate improvement methods used in the past. Furthermore, the attendance rate improvement unit can select the most efficient attendance rate improvement method from the user's past attendance history. The attendance rate improvement unit can analyze the user's past attendance history and select the most efficient attendance rate improvement method. In addition, the attendance rate improvement unit can analyze the user's past attendance history and propose the optimal means of improving attendance. The attendance rate improvement unit can analyze the user's past attendance history and propose the optimal means of improving attendance. As a result, the attendance rate improvement unit can select the optimal attendance rate improvement method based on the user's past attendance history. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can entrust the analysis of the user's past attendance history to AI.

[0056] The attendance rate improvement unit can select the optimal attendance rate improvement method by considering the user's device information when improving attendance rates. For example, if the user is using a smartphone, the attendance rate improvement unit can provide an attendance rate improvement method optimized for smartphones. The attendance rate improvement unit can consider the user's device information and provide an attendance rate improvement method optimized for smartphones if the user is using a smartphone. Furthermore, if the user is using a tablet, the attendance rate improvement unit can provide an attendance rate improvement method optimized for tablets if the user is using a tablet. In addition, if the user is using a desktop, the attendance rate improvement unit can provide an attendance rate improvement method optimized for desktops. The attendance rate improvement unit can consider the user's device information and provide an attendance rate improvement method optimized for desktops if the user is using a desktop. This allows the attendance rate improvement unit to select the optimal attendance rate improvement method based on the user's device information. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can leave the consideration of the user's device information to AI.

[0057] The attendance rate improvement unit can select the optimal attendance rate improvement method by referring to the user's calendar information when improving attendance rates. For example, the attendance rate improvement unit can refer to appointments registered in the user's calendar and propose an attendance rate improvement method. The attendance rate improvement unit can refer to the user's calendar information and propose an optimal attendance rate improvement method based on the registered appointments. Furthermore, the attendance rate improvement unit can propose an attendance rate improvement method related to a specific event from the user's calendar information. The attendance rate improvement unit can refer to the user's calendar information and propose an attendance rate improvement method related to a specific event. In addition, the attendance rate improvement unit can propose an optimal attendance rate improvement method tailored to the appointments based on the user's calendar information. The attendance rate improvement unit can refer to the user's calendar information and propose an optimal attendance rate improvement method tailored to the appointments. As a result, the attendance rate improvement unit can select the optimal attendance rate improvement method based on the user's calendar information. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can entrust the referencing of the user's calendar information to AI.

[0058] The attendance rate improvement unit can analyze the user's social media activity and select the optimal method for improving attendance rates. For example, the attendance rate improvement unit can suggest ways for the user to attend events that the user has shown interest in on social media. The attendance rate improvement unit can analyze the user's social media activity and suggest ways for the user to attend events that the user has shown interest in. Furthermore, the attendance rate improvement unit can suggest ways for the user to attend events that the user's social media friends are attending. The attendance rate improvement unit can analyze the user's social media activity and suggest ways for the user to attend events that the user's friends are attending. In addition, the attendance rate improvement unit can suggest ways for the user to attend events of groups that the user follows on social media. The attendance rate improvement unit can analyze the user's social media activity and suggest ways for the user to attend events of groups that the user follows. This allows the attendance rate improvement unit to select the optimal method for improving attendance rates based on the user's social media activity. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or not. For example, the attendance rate improvement unit can entrust the analysis of the user's social media activity to AI.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The meeting participation system can also include a "translation unit." This unit can translate what is said during the meeting in real time and provide it to participants. For example, if a meeting is conducted in English but there are Japanese-speaking participants, the translation unit can translate the English statements into Japanese and display them to the participants. The translation unit can also create meeting minutes in multiple languages ​​and provide them to participants. For example, after the meeting, minutes can be automatically generated in languages ​​such as English, Japanese, and French and distributed to participants. Furthermore, the translation unit can also translate chat messages during the meeting in real time and display them to participants. This facilitates smoother communication between participants who speak different languages ​​and improves the productivity of international meetings.

[0061] The meeting participation system can also include a "feedback section." This section can collect and analyze feedback from participants after the meeting. For example, it can automatically send out questionnaires after the meeting to gather participant satisfaction levels and areas for improvement. Furthermore, the feedback section can analyze the collected feedback and propose improvements for future meetings. For instance, it can suggest improvements to meeting procedures and agenda setting based on participant feedback. Additionally, the feedback section can share the feedback results with participants, increasing transparency. This ultimately improves the quality of meetings and increases participant satisfaction.

[0062] The meeting participation system can also include a "health management department." This department can monitor participants' health during the meeting and provide appropriate advice. For example, it can measure participants' heart rate and stress levels, monitoring their health in real time. The health management department can also display notifications encouraging participants to stretch or take breaks periodically to prevent prolonged sitting. Furthermore, the health management department can provide a health report after the meeting to aid in participant health management. This enables efficient meeting management while maintaining participants' health.

[0063] The meeting participation system can also include a "networking section." This section can provide functions to facilitate interaction among meeting participants. For example, it can share participant profiles before the meeting and match participants with shared interests. It can also automatically set up breakout sessions during the meeting to promote smaller group discussions. Furthermore, it can provide a function to exchange contact information among participants after the meeting, supporting continued interaction. This enhances networking among participants and increases the value of the meeting.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit synchronizes with the user's calendar. The data collection unit can synchronize with, for example, a calendar. The data collection unit can select the appropriate synchronization method depending on the type of calendar. Step 2: The notification unit displays a notification based on the meeting start time obtained by the collection unit. The notification unit can display notifications in various ways, such as pop-up notifications, email notifications, and push notifications. As the meeting start time approaches, the notification unit can display reminders 10 minutes, 5 minutes, and 1 minute beforehand. For example, a pop-up notification will appear on the user's desktop screen. An email notification will be sent to the user's email address. A push notification will appear on the user's smartphone. Step 3: Participants automatically join the meeting based on reminders displayed by the notification unit. Participants can, for example, automatically launch the meeting tool and join the meeting without clicking a meeting link. Participants can automatically launch the meeting tool, log in, and configure camera and microphone settings. For example, a participant can obtain a meeting link and automatically launch the meeting tool. Once the meeting tool is launched, the participant can automatically configure the user's camera and microphone.

[0066] (Example of form 2) The meeting participation system according to an embodiment of the present invention is a system that improves work efficiency by reducing the time spent preparing for and attending meetings, by allowing participants to automatically join online meetings at the start time of the meeting. The meeting participation system synchronizes with the user's calendar and automatically displays notifications as the meeting start time approaches. For example, reminders pop up 10 minutes, 5 minutes, and 1 minute before the meeting to inform the user of the start of the meeting. After the reminder is displayed, the user can seamlessly join the meeting without clicking on a meeting link. For example, when the reminder is displayed, the user can automatically join the online meeting without any action required from the user. This mechanism reduces the time spent preparing for and attending meetings, thereby improving work efficiency. Furthermore, this mechanism improves meeting productivity. By avoiding the time spent manually searching for meeting links and technical problems, the time from the start of the meeting to getting to the main topic can be shortened. For example, meetings can start smoothly without being late for the scheduled start time. In addition, attendance and participation rates improve. Reminders and automatic participation functions reduce missed meetings and lateness, allowing all relevant parties to participate in all important meetings in a timely manner. This improves the quality of discussions and the speed of decision-making. The system automates meeting preparation time (checking links, logging into meeting tools, setting up cameras and microphones, etc.), eliminating the need for manual operation each time. For example, it can save 5-10 minutes per meeting, and companies that hold multiple meetings per week can expect to save hundreds of hours annually. As a result, meeting participation systems can reduce the time spent on meeting preparation and attendance, thereby improving work efficiency.

[0067] The meeting participation system according to the embodiment comprises a collection unit, a notification unit, and a participation unit. The collection unit synchronizes with the user's calendar. The collection unit can, for example, synchronize with a calendar. The collection unit can select an appropriate synchronization method depending on the type of calendar. The notification unit displays a notification based on the meeting start time acquired by the collection unit. The notification unit can display notifications by methods such as pop-up notifications, email notifications, and push notifications. As the meeting start time approaches, the notification unit can display reminders 10 minutes, 5 minutes, and 1 minute before. For example, a pop-up notification is displayed on the user's desktop screen. An email notification is sent to the user's email address. A push notification is displayed on the user's smartphone. The participation unit automatically joins the meeting based on the reminder displayed by the notification unit. The participation unit can, for example, automatically launch the meeting tool and join the meeting without clicking a meeting link. The participation unit can perform actions such as automatically launching the meeting tool, logging in, and setting up the camera and microphone. For example, the participation unit acquires the meeting link and automatically launches the meeting tool. When the meeting tool is launched, the participant automatically configures the user's camera and microphone. This allows the meeting participation system according to the embodiment to enable users to join meetings smoothly, thereby improving work efficiency.

[0068] The data collection unit synchronizes with the user's calendar. For example, it can synchronize with various calendar systems. This allows the data collection unit to support diverse calendar systems and centrally manage the user's meeting information. Furthermore, the data collection unit regularly updates calendar information, reflecting newly added or modified meeting information in real time. This ensures users always have access to the latest meeting information, enabling smooth meeting participation.

[0069] The notification unit displays notifications based on the meeting start time acquired by the data collection unit. Specifically, as the meeting start time approaches, reminders can be displayed 10 minutes, 5 minutes, and 1 minute beforehand. Pop-up notifications appear on the user's desktop screen, visually informing them of the meeting's start. Email notifications are sent to the user's email address and can be checked through their email client. Push notifications appear on the user's smartphone, informing them of the meeting's start via their mobile device. This ensures that users do not miss the start of a meeting, regardless of the device they are using. Furthermore, the notification unit can customize notification methods according to user preferences. For example, only pop-up notifications can be displayed for certain meetings, while email and push notifications can be used in combination for other meetings. The notification unit can also adjust the frequency and timing of notifications according to the importance of the meeting. For example, reminders can be displayed 30 minutes before the start of important meetings and 10 minutes before the start of regular meetings. This allows the notification unit to provide flexible notifications tailored to user needs and ensure that users are notified of the start of meetings.

[0070] The participant unit automatically joins the meeting based on a reminder displayed by the notification unit. Specifically, it can automatically launch the meeting tool and join the meeting without clicking a meeting link. The participant unit obtains the meeting link and automatically launches the meeting tool. For example, it can support a meeting tool, analyze the meeting link, select the appropriate tool, and launch it. Once the meeting tool is launched, the participant unit automatically configures the user's camera and microphone. Specifically, it adjusts the camera resolution and microphone volume to optimal levels to allow the user to join the meeting smoothly. The participant unit can also save the user's login information in advance and automatically log in to the meeting tool. This eliminates the need for the user to enter login information. Furthermore, the participant unit can not only automatically join the meeting at the start time but also automatically leave the meeting at the end time. This allows the user to concentrate on their work without being aware of the start and end of meetings. The participant unit manages the meeting schedule based on the user's calendar information and can join the meeting at the appropriate time. As a result, the meeting participation system according to this embodiment allows users to join meetings smoothly and improves work efficiency.

[0071] The Preparation Department is a department dedicated to reducing meeting preparation time. The Preparation Department can automate preparation tasks such as verifying meeting links, logging into meeting tools, and setting up cameras and microphones. The Preparation Department can automate preparation tasks through methods such as executing scripts or using APIs. The Preparation Department can automatically verify meeting links and log into meeting tools. For example, the Preparation Department can execute a script to obtain meeting links and automatically log into meeting tools. Furthermore, the Preparation Department can automatically configure camera and microphone settings. For example, the Preparation Department can utilize APIs to automatically configure camera and microphone settings. This allows the Preparation Department to reduce meeting preparation time and improve operational efficiency. Some or all of the above processes in the Preparation Department may be performed using AI, or not. For example, the Preparation Department can have AI perform tasks such as verifying meeting links, logging into meeting tools, and configuring cameras and microphones.

[0072] The preparation team can automate preparatory tasks such as verifying meeting links, logging into meeting tools, and setting up cameras and microphones. For example, the preparation team can automate verifying meeting links. The preparation team can automatically obtain meeting links and notify users. For example, the preparation team can execute a script to obtain meeting links and notify users of the obtained links. The preparation team can also automate logging into meeting tools. The preparation team can execute a script to automatically log into meeting tools. For example, the preparation team can obtain login information for meeting tools and execute a script to automatically log in. The preparation team can also automate setting up cameras and microphones. The preparation team can utilize APIs to automatically set up cameras and microphones. For example, the preparation team can use APIs to automatically set up cameras and microphones and complete the setup. This automates the preparatory work for meetings, eliminating the need for manual operation. Some or all of the above processes in the preparation team may be performed using AI, for example, or not. For example, the preparation team can have AI perform tasks such as verifying meeting links, logging into meeting tools, and setting up cameras and microphones.

[0073] The Productivity Improvement Department is a department dedicated to improving the productivity of meetings. For example, the Productivity Improvement Department can shorten the time from the start of a meeting to getting to the main topic by avoiding the time spent manually searching for meeting links and technical problems. The Productivity Improvement Department can improve meeting productivity through methods such as pre-sharing of agendas and timekeeping. The Productivity Improvement Department can share meeting agendas in advance, allowing participants to understand the meeting content beforehand. For example, the Productivity Improvement Department can share meeting agendas via email in advance. Furthermore, the Productivity Improvement Department can ensure smooth meeting progress by performing timekeeping. For example, the Productivity Improvement Department can assign a timekeeper to manage the meeting's progress and ensure smooth meeting flow. This allows the Productivity Improvement Department to improve meeting productivity and increase operational efficiency. Some or all of the above processes in the Productivity Improvement Department may be performed using AI, or not. For example, the Productivity Improvement Department could have AI perform pre-sharing of agendas and timekeeping.

[0074] The Productivity Improvement Department can shorten the time from the start of a meeting to getting to the main topic by avoiding the time spent manually searching for meeting links and technical problems. For example, the Productivity Improvement Department can share meeting links in advance, reducing the time participants spend searching for them. The Productivity Improvement Department can share meeting links via email in advance. The Productivity Improvement Department can also perform technical checks in advance to avoid technical problems. The Productivity Improvement Department can perform technical checks before meetings to prevent technical problems from occurring. For example, the Productivity Improvement Department can check the operation of cameras and microphones before meetings. The Productivity Improvement Department can also ensure smooth meeting progress to shorten the time from the start of a meeting to getting to the main topic. The Productivity Improvement Department can ensure smooth meeting progress by assigning a timekeeper to manage the meeting progress. For example, the Productivity Improvement Department can ensure smooth meeting progress by assigning a timekeeper to manage the meeting progress. In this way, the Productivity Improvement Department can improve meeting productivity and streamline operations. Some or all of the above processes in the Productivity Improvement Department may be performed using AI, for example, or not using AI. For example, the productivity improvement department can have AI perform tasks such as pre-sharing meeting links, technical checks, and timekeeping.

[0075] The Attendance Improvement Department is a department dedicated to improving attendance and participation rates. For example, the Attendance Improvement Department can reduce missed meetings and lateness through features such as reminders and automatic join functions. The Attendance Improvement Department can set reminders and notify users as the meeting start time approaches. For example, it can display reminders 10 minutes, 5 minutes, and 1 minute before the meeting. Furthermore, the Attendance Improvement Department can provide an automatic join function, allowing users to automatically join meetings without clicking a meeting link. For example, it can retrieve the meeting link, automatically launch the meeting tool, and join the meeting. This allows the Attendance Improvement Department to reduce missed meetings and lateness, thereby improving attendance and participation rates. Some or all of the above processes in the Attendance Improvement Department may be performed using AI, or not. For example, the Attendance Improvement Department can delegate the setting of reminders and the execution of the automatic join function to AI.

[0076] The attendance rate improvement unit can reduce missed meetings and lateness through reminders and automatic join functions. For example, the attendance rate improvement unit can set reminders and notify users as the meeting start time approaches. The attendance rate improvement unit can display reminders 10 minutes, 5 minutes, and 1 minute before the meeting. In addition, the attendance rate improvement unit can provide an automatic join function, allowing users to automatically join meetings without clicking a meeting link. For example, the attendance rate improvement unit can obtain the meeting link, automatically launch the meeting tool, and join the meeting. This reduces missed meetings and lateness, improving meeting attendance rates. Some or all of the above processes in the attendance rate improvement unit may be performed using AI, for example, or not. For example, the attendance rate improvement unit can entrust the setting of reminders and the execution of the automatic join function to AI.

[0077] The data collection unit can estimate the user's emotions and adjust the calendar synchronization timing based on the estimated emotions. The data collection unit can estimate the user's emotions using methods such as facial recognition, voice analysis, and text analysis. The data collection unit can estimate the user's emotions and, if the user is stressed, delay calendar synchronization and reduce the frequency of notifications. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the data collection unit can analyze the user's text data and estimate their emotions. For example, the data collection unit can analyze the content of the user's emails and chats and estimate their emotions. This allows the data collection unit to adjust the calendar synchronization timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can entrust the estimation of user emotions to AI.

[0078] The data collection unit can analyze the user's past meeting participation history and select the optimal synchronization method. For example, the data collection unit can prioritize the synchronization of meetings that the user has frequently attended in the past. The data collection unit can analyze the user's past meeting participation history and prioritize the synchronization of meetings that the user has frequently attended. It can also exclude meetings that the user has not attended in the past from the synchronization. The data collection unit can analyze the user's past meeting participation history and exclude meetings that the user has not attended from the synchronization. Furthermore, the data collection unit can concentrate the synchronization on specific time periods based on the user's past participation history. The data collection unit can analyze the user's past meeting participation history and concentrate the synchronization on specific time periods. This allows the data collection unit to select the optimal synchronization method based on the user's past meeting participation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can entrust the analysis of the user's past meeting participation history to AI.

[0079] The data collection unit can filter the calendar based on the user's current projects and areas of interest during synchronization. For example, the data collection unit can synchronize only meetings related to the user's current projects. The data collection unit can prioritize the synchronization of meetings related to the user's current projects. The data collection unit can also prioritize the synchronization of relevant meetings based on the user's areas of interest. The data collection unit can also prioritize the synchronization of relevant meetings based on the user's areas of interest. Furthermore, the data collection unit can exclude meetings in areas that the user has not expressed interest in during synchronization. The data collection unit can exclude meetings in areas that the user has not expressed interest in during synchronization. This allows the data collection unit to synchronize the calendar based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can leave the filtering of the user's projects and areas of interest to AI.

[0080] The data collection unit can estimate the user's emotions and determine the priority of calendar events to synchronize based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone less important events. The data collection unit can estimate the user's emotions and postpone less important events if the user is stressed. Also, if the user is relaxed, the data collection unit can treat all events equally. The data collection unit can estimate the user's emotions and treat all events equally if the user is relaxed. Furthermore, if the user is busy, the data collection unit can prioritize and synchronize the most important events. The data collection unit can estimate the user's emotions and prioritize and synchronize the most important events if the user is busy. This allows the data collection unit to determine the priority of calendar events according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can entrust the estimation of user emotions to AI.

[0081] The data collection unit can prioritize the synchronization of highly relevant events when synchronizing the calendar, taking into account the user's geographical location. For example, the data collection unit can prioritize the synchronization of meetings held near the user's current location. The data collection unit can prioritize the synchronization of meetings held near the user's current location, taking into account the user's geographical location. Furthermore, if the user is on the move, the data collection unit can prioritize the synchronization of meetings held at their destination. The data collection unit can prioritize the synchronization of meetings held at their destination, taking into account the user's geographical location. In addition, if the user is in a specific region, the data collection unit can prioritize the synchronization of events related to that region. The data collection unit can prioritize the synchronization of events related to that region, taking into account the user's geographical location. This allows the data collection unit to prioritize the synchronization of highly relevant events based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can leave the consideration of the user's geographical location to AI.

[0082] The data collection unit can analyze the user's social media activity and synchronize relevant events when synchronizing the calendar. For example, the data collection unit can prioritize synchronizing events that the user has shown interest in on social media. The data collection unit can analyze the user's social media activity and prioritize synchronizing events that the user has shown interest in. The data collection unit can also prioritize synchronizing events that the user's social media friends are participating in. The data collection unit can analyze the user's social media activity and prioritize synchronizing events that the user's friends are participating in. Furthermore, the data collection unit can prioritize synchronizing events that the user is following on social media. The data collection unit can analyze the user's social media activity and prioritize synchronizing events that the user is following. This allows the data collection unit to synchronize relevant events based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can entrust the analysis of the user's social media activity to AI.

[0083] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those estimated emotions. The notification unit can estimate the user's emotions using methods such as facial recognition, voice analysis, and text analysis. If the user is tense, the notification unit can display notifications in a calmer tone. For example, the notification unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the notification unit can analyze the user's text data and estimate their emotions. For example, it can analyze the content of the user's emails or chats and estimate their emotions. This allows the notification unit to adjust the way notifications are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can entrust the estimation of the user's emotions to AI.

[0084] The notification unit can adjust the level of detail of a notification based on the importance of the meeting. For example, the notification unit can display a detailed notification for important meetings. The notification unit can display a detailed notification based on the importance of the meeting. The notification unit can also display a concise notification for less important meetings. The notification unit can display a concise notification based on the importance of the meeting. Furthermore, the notification unit can customize the content of the notification according to the importance of the meeting. The notification unit can customize the content of the notification according to the importance of the meeting. This allows the notification unit to adjust the level of detail of the notification according to the importance of the meeting. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can entrust the adjustment of the level of detail of the notification based on the importance of the meeting to AI.

[0085] The notification unit can apply different notification algorithms depending on the meeting category when issuing notifications. For example, the notification unit can display a formal notification for business meetings. The notification unit can display formal notifications depending on the meeting category. The notification unit can also display relaxed notifications for casual meetings. The notification unit can display relaxed notifications depending on the meeting category. Furthermore, the notification unit can display quick and prominent notifications for urgent meetings. The notification unit can display quick and prominent notifications depending on the meeting category. This allows the notification unit to apply different notification algorithms depending on the meeting category. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can entrust the application of notification algorithms according to the meeting category to AI.

[0086] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can delay the timing of notifications. The notification unit can estimate the user's emotions and delay the timing of notifications if the user is feeling stressed. Also, if the user is relaxed, the notification unit can speed up the timing of notifications if the user is relaxed. Furthermore, if the user is busy, the notification unit can prioritize displaying only important notifications. The notification unit can estimate the user's emotions and prioritize displaying only important notifications if the user is busy. In this way, the notification unit can adjust the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can entrust the estimation of the user's emotions to AI.

[0087] The notification unit can determine the priority of notifications based on the meeting start time. For example, the notification unit can display notifications preferentially when the meeting start time is approaching. The notification unit can display notifications preferentially when the meeting start time is approaching. The notification unit can also lower the priority of notifications when the meeting start time is far away. The notification unit can lower the priority of notifications when the meeting start time is far away. Furthermore, the notification unit can customize the content of notifications according to the meeting start time. The notification unit can customize the content of notifications based on the meeting start time. This allows the notification unit to determine the priority of notifications based on the meeting start time. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can leave the determination of notification priority based on the meeting start time to AI.

[0088] The notification unit can adjust the order of notifications based on the relevance of the meetings when it sends notifications. For example, the notification unit can prioritize notifications for meetings that are important to the user. The notification unit can prioritize notifications for meetings that are important to the user based on the relevance of the meetings. The notification unit can also postpone notifications for meetings that are less relevant to the user based on the relevance of the meetings. Furthermore, the notification unit can customize the order of notifications according to the relevance of the meetings. The notification unit can customize the order of notifications based on the relevance of the meetings. This allows the notification unit to adjust the order of notifications based on the relevance of the meetings. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can entrust the adjustment of the order of notifications based on the relevance of the meetings to AI.

[0089] The participant can estimate the user's emotions and adjust the automatic participation method based on the estimated user emotions. For example, if the user is nervous, the participant can provide a simple automatic participation method. The participant can estimate the user's emotions and provide a simple automatic participation method if the user is nervous. Furthermore, if the user is relaxed, the participant can provide a detailed automatic participation method. The participant can estimate the user's emotions and provide a detailed automatic participation method if the user is relaxed. In addition, if the user is in a hurry, the participant can provide a rapid automatic participation method. The participant can estimate the user's emotions and provide a rapid automatic participation method if the user is in a hurry. This allows the participant to adjust the automatic participation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the participant may be performed using AI, for example, or without AI. For example, the participating team can entrust the estimation of user emotions to AI.

[0090] The participation unit can analyze the user's past participation history and select the optimal participation method during automatic participation. For example, the participation unit can suggest the optimal participation method based on the participation methods the user has used in the past. The participation unit can analyze the user's past participation history and suggest the optimal participation method based on the participation methods used in the past. Furthermore, the participation unit can select the most efficient participation method from the user's past participation history. The participation unit can analyze the user's past participation history and select the most efficient participation method. In addition, the participation unit can analyze the user's past participation history and suggest the optimal means of participation. The participation unit can analyze the user's past participation history and suggest the optimal means of participation. As a result, the participation unit can select the optimal participation method based on the user's past participation history. Some or all of the above processing in the participation unit may be performed using AI, for example, or without AI. For example, the participation unit can entrust the analysis of the user's past participation history to AI.

[0091] The participation unit can customize the means of participation based on the user's current living situation when automatically joining. For example, if the user is at home, the participation unit can provide a means of participation for home use. The participation unit can provide a means of participation for home use if the user is at home, based on the user's current living situation. Furthermore, if the user is out, the participation unit can provide a means of participation for office use if the user is at the office, based on the user's current living situation. In this way, the participation unit can customize the means of participation based on the user's current living situation. Some or all of the above processing in the participation unit may be performed using AI, for example, or not using AI. For example, the participation unit can leave the customization of the means of participation based on the user's living situation to AI.

[0092] The participant can estimate the user's emotions and determine the priority of automatic participation based on the estimated emotions. For example, if the user is feeling stressed, the participant can postpone automatic participation in less important meetings. The participant can estimate the user's emotions and postpone automatic participation in less important meetings if the user is feeling stressed. Also, if the user is relaxed, the participant can automatically participate in all meetings equally. The participant can estimate the user's emotions and automatically participate in all meetings equally if the user is relaxed. Furthermore, if the user is busy, the participant can prioritize automatic participation in the most important meetings. The participant can estimate the user's emotions and automatically participate in the most important meetings if the user is busy. This allows the participant to determine the priority of automatic participation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the participating unit may be performed using AI, for example, or without AI. For example, the participating unit can entrust the estimation of the user's emotions to AI.

[0093] The participant function can select the optimal participation method by considering the user's geographical location when automatically joining a meeting. For example, the participant function can automatically prioritize joining meetings held near the user's current location. The participant function can automatically prioritize joining meetings held near the user's current location by considering the user's geographical location. Furthermore, if the user is traveling, the participant function can automatically join meetings held at their destination. The participant function can automatically join meetings held at their destination if the user is traveling, considering the user's geographical location. In addition, if the user is in a specific region, the participant function can automatically join meetings related to that region. The participant function can automatically join meetings related to that region if the user is in a specific region by considering the user's geographical location. This allows the participant function to select the optimal participation method based on the user's geographical location. Some or all of the above processing in the participant function may be performed using AI, for example, or without AI. For example, the participant function can leave the consideration of the user's geographical location to AI.

[0094] The participation unit can analyze the user's social media activity and suggest participation methods when automatically joining. For example, the participation unit can automatically join meetings that the user has shown interest in on social media. The participation unit can analyze the user's social media activity and automatically join meetings that the user has shown interest in. Furthermore, the participation unit can automatically join meetings that the user's social media friends are attending. The participation unit can analyze the user's social media activity and automatically join meetings that friends are attending. In addition, the participation unit can automatically join meetings of groups that the user follows on social media. The participation unit can analyze the user's social media activity and automatically join meetings of groups that the user follows. This allows the participation unit to suggest the most suitable participation method based on the user's social media activity. Some or all of the above processing in the participation unit may be performed using AI, for example, or not. For example, the participation unit can entrust the analysis of the user's social media activity to AI.

[0095] The preparation unit can estimate the user's emotions and adjust the preparation process based on the estimated emotions. For example, if the user is nervous, the preparation unit can provide simple preparation tasks. The preparation unit can estimate the user's emotions and provide simple preparation tasks if the user is nervous. Furthermore, if the user is relaxed, the preparation unit can provide detailed preparation tasks. The preparation unit can estimate the user's emotions and provide detailed preparation tasks if the user is relaxed. In addition, if the user is in a hurry, the preparation unit can provide quick preparation tasks. The preparation unit can estimate the user's emotions and provide quick preparation tasks if the user is in a hurry. This allows the preparation unit to adjust the preparation process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation department can entrust the estimation of user emotions to AI.

[0096] The preparation unit can analyze the user's past preparation history and select the optimal preparation method during the preparation process. For example, the preparation unit can propose the optimal preparation method based on the preparation methods the user has used in the past. The preparation unit can analyze the user's past preparation history and propose the optimal preparation method based on the methods used in the past. Furthermore, the preparation unit can select the most efficient preparation method from the user's past preparation history. The preparation unit can analyze the user's past preparation history and select the most efficient preparation method. In addition, the preparation unit can analyze the user's past preparation history and propose the optimal preparation means. This allows the preparation unit to select the optimal preparation method based on the user's past preparation history. Some or all of the above processing in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation unit can entrust the analysis of the user's past preparation history to AI.

[0097] The preparation unit can estimate the user's emotions and determine the priority of preparation tasks based on the estimated emotions. For example, if the user is stressed, the preparation unit can postpone less important preparation tasks. The preparation unit can estimate the user's emotions and postpone less important preparation tasks if the user is stressed. Also, if the user is relaxed, the preparation unit can treat all preparation tasks equally. The preparation unit can estimate the user's emotions and treat all preparation tasks equally if the user is relaxed. Furthermore, if the user is busy, the preparation unit can prioritize the most important preparation tasks. The preparation unit can estimate the user's emotions and prioritize the most important preparation tasks if the user is busy. This allows the preparation unit to determine the priority of preparation tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation unit can entrust the estimation of the user's emotions to AI.

[0098] The preparation unit can select the optimal preparation method during the preparation process, taking into account the user's device information. For example, if the user is using a smartphone, the preparation unit can provide a preparation method optimized for smartphones. The preparation unit can consider the user's device information and provide a preparation method optimized for smartphones if the user is using a smartphone. Furthermore, if the user is using a tablet, the preparation unit can provide a preparation method optimized for tablets if the user is using a tablet. In addition, if the user is using a desktop, the preparation unit can provide a preparation method optimized for desktops if the user is using a desktop. The preparation unit can consider the user's device information and provide a preparation method optimized for desktops if the user is using a desktop. This allows the preparation unit to select the optimal preparation method based on the user's device information. Some or all of the above processing in the preparation unit may be performed using AI, for example, or without AI. For example, the preparation unit can leave the consideration of the user's device information to AI.

[0099] The productivity improvement unit can estimate the user's emotions and adjust productivity improvement methods based on the estimated emotions. For example, if the user is feeling stressed, the productivity improvement unit can provide a relaxing environment. The productivity improvement unit can estimate the user's emotions and provide a relaxing environment if the user is feeling stressed. Furthermore, if the user is relaxed, the productivity improvement unit can provide an environment that enhances concentration. The productivity improvement unit can estimate the user's emotions and provide an environment that enhances concentration if the user is relaxed. In addition, if the user is in a hurry, the productivity improvement unit can provide an environment that allows them to work quickly. The productivity improvement unit can estimate the user's emotions and provide an environment that allows them to work quickly if the user is in a hurry. This allows the productivity improvement unit to adjust productivity improvement methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the productivity improvement unit may be performed using AI, for example, or without AI. For example, the productivity improvement unit can entrust the estimation of user emotions to AI.

[0100] The productivity improvement unit can analyze the user's past meeting history and select the optimal productivity improvement method when improving productivity. For example, the productivity improvement unit can propose the optimal method based on the productivity improvement methods the user has used in the past. The productivity improvement unit can analyze the user's past meeting history and propose the optimal method based on the methods used in the past. Furthermore, the productivity improvement unit can select the most efficient productivity improvement method from the user's past meeting history. The productivity improvement unit can analyze the user's past meeting history and select the most efficient productivity improvement method. In addition, the productivity improvement unit can analyze the user's past meeting history and propose the optimal means of productivity improvement. This allows the productivity improvement unit to select the optimal productivity improvement method based on the user's past meeting history. Some or all of the above processing in the productivity improvement unit may be performed using AI, for example, or without AI. For example, the productivity improvement unit can entrust the analysis of the user's past meeting history to AI.

[0101] The productivity improvement unit can estimate the user's emotions and determine productivity improvement priorities based on the estimated emotions. For example, if the user is stressed, the productivity improvement unit can postpone less important tasks. The productivity improvement unit can estimate the user's emotions and postpone less important tasks if the user is stressed. Furthermore, if the user is relaxed, the productivity improvement unit can treat all tasks equally. The productivity improvement unit can estimate the user's emotions and treat all tasks equally if the user is relaxed. In addition, if the user is busy, the productivity improvement unit can prioritize the most important tasks. The productivity improvement unit can estimate the user's emotions and prioritize the most important tasks if the user is busy. This allows the productivity improvement unit to determine productivity improvement priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the productivity improvement unit may be performed using AI, for example, or without AI. For example, the productivity improvement unit can entrust the estimation of user emotions to AI.

[0102] The productivity improvement unit can select the optimal productivity improvement method when improving productivity, taking into account the user's device information. For example, if the user is using a smartphone, the productivity improvement unit can provide a productivity improvement method optimized for smartphones. The productivity improvement unit can consider the user's device information and provide a productivity improvement method optimized for smartphones if the user is using a smartphone. Furthermore, if the user is using a tablet, the productivity improvement unit can provide a productivity improvement method optimized for tablets if the user is using a tablet. The productivity improvement unit can consider the user's device information and provide a productivity improvement method optimized for tablets if the user is using a tablet. In addition, if the user is using a desktop, the productivity improvement unit can provide a productivity improvement method optimized for desktops if the user is using a desktop. The productivity improvement unit can consider the user's device information and provide a productivity improvement method optimized for desktops if the user is using a desktop. This allows the productivity improvement unit to select the optimal productivity improvement method based on the user's device information. Some or all of the above processing in the productivity improvement unit may be performed using AI, for example, or without AI. For example, the productivity improvement unit can leave the consideration of the user's device information to AI.

[0103] The attendance rate improvement unit can estimate the user's emotions and adjust the attendance rate improvement method based on the estimated user's emotions. For example, if the user is feeling stressed, the attendance rate improvement unit can provide a relaxing environment. The attendance rate improvement unit can estimate the user's emotions and provide a relaxing environment if the user is feeling stressed. Furthermore, if the user is relaxed, the attendance rate improvement unit can provide an environment that enhances concentration. The attendance rate improvement unit can estimate the user's emotions and provide an environment that enhances concentration if the user is relaxed. In addition, if the user is in a hurry, the attendance rate improvement unit can provide an environment that allows them to work quickly. The attendance rate improvement unit can estimate the user's emotions and provide an environment that allows them to work quickly if the user is in a hurry. As a result, the attendance rate improvement unit can adjust the attendance rate improvement method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can entrust the estimation of user emotions to AI.

[0104] The attendance rate improvement unit can analyze the user's past attendance history to select the optimal attendance rate improvement method when improving attendance rates. For example, the attendance rate improvement unit can propose the optimal method based on the attendance rate improvement methods the user has used in the past. The attendance rate improvement unit can analyze the user's past attendance history and propose the optimal method based on the attendance rate improvement methods used in the past. Furthermore, the attendance rate improvement unit can select the most efficient attendance rate improvement method from the user's past attendance history. The attendance rate improvement unit can analyze the user's past attendance history and select the most efficient attendance rate improvement method. In addition, the attendance rate improvement unit can analyze the user's past attendance history and propose the optimal means of improving attendance. The attendance rate improvement unit can analyze the user's past attendance history and propose the optimal means of improving attendance. As a result, the attendance rate improvement unit can select the optimal attendance rate improvement method based on the user's past attendance history. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can entrust the analysis of the user's past attendance history to AI.

[0105] The attendance rate improvement unit can estimate the user's emotions and determine the priority of attendance improvement based on the estimated emotions. For example, if the user is feeling stressed, the attendance rate improvement unit can postpone attendance at less important meetings. The attendance rate improvement unit can estimate the user's emotions and postpone attendance at less important meetings if the user is feeling stressed. Also, if the user is relaxed, the attendance rate improvement unit can ensure equal attendance at all meetings. The attendance rate improvement unit can estimate the user's emotions and ensure equal attendance at all meetings if the user is relaxed. Furthermore, if the user is busy, the attendance rate improvement unit can prioritize attendance at the most important meetings. The attendance rate improvement unit can estimate the user's emotions and prioritize attendance at the most important meetings if the user is busy. In this way, the attendance rate improvement unit can determine the priority of attendance improvement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can entrust the estimation of user emotions to AI.

[0106] The attendance rate improvement unit can select the optimal attendance rate improvement method by considering the user's device information when improving attendance rates. For example, if the user is using a smartphone, the attendance rate improvement unit can provide an attendance rate improvement method optimized for smartphones. The attendance rate improvement unit can consider the user's device information and provide an attendance rate improvement method optimized for smartphones if the user is using a smartphone. Furthermore, if the user is using a tablet, the attendance rate improvement unit can provide an attendance rate improvement method optimized for tablets if the user is using a tablet. In addition, if the user is using a desktop, the attendance rate improvement unit can provide an attendance rate improvement method optimized for desktops. The attendance rate improvement unit can consider the user's device information and provide an attendance rate improvement method optimized for desktops if the user is using a desktop. This allows the attendance rate improvement unit to select the optimal attendance rate improvement method based on the user's device information. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can leave the consideration of the user's device information to AI.

[0107] The attendance rate improvement unit can select the optimal attendance rate improvement method by referring to the user's calendar information when improving attendance rates. For example, the attendance rate improvement unit can refer to appointments registered in the user's calendar and propose an attendance rate improvement method. The attendance rate improvement unit can refer to the user's calendar information and propose an optimal attendance rate improvement method based on the registered appointments. Furthermore, the attendance rate improvement unit can propose an attendance rate improvement method related to a specific event from the user's calendar information. The attendance rate improvement unit can refer to the user's calendar information and propose an attendance rate improvement method related to a specific event. In addition, the attendance rate improvement unit can propose an optimal attendance rate improvement method tailored to the appointments based on the user's calendar information. The attendance rate improvement unit can refer to the user's calendar information and propose an optimal attendance rate improvement method tailored to the appointments. As a result, the attendance rate improvement unit can select the optimal attendance rate improvement method based on the user's calendar information. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or without AI. For example, the attendance rate improvement unit can entrust the referencing of the user's calendar information to AI.

[0108] The attendance rate improvement unit can analyze the user's social media activity and select the optimal method for improving attendance rates. For example, the attendance rate improvement unit can suggest ways for the user to attend events that the user has shown interest in on social media. The attendance rate improvement unit can analyze the user's social media activity and suggest ways for the user to attend events that the user has shown interest in. Furthermore, the attendance rate improvement unit can suggest ways for the user to attend events that the user's social media friends are attending. The attendance rate improvement unit can analyze the user's social media activity and suggest ways for the user to attend events that the user's friends are attending. In addition, the attendance rate improvement unit can suggest ways for the user to attend events of groups that the user follows on social media. The attendance rate improvement unit can analyze the user's social media activity and suggest ways for the user to attend events of groups that the user follows. This allows the attendance rate improvement unit to select the optimal method for improving attendance rates based on the user's social media activity. Some or all of the above processing in the attendance rate improvement unit may be performed using AI, for example, or not. For example, the attendance rate improvement unit can entrust the analysis of the user's social media activity to AI.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The meeting participation system can also include a "translation unit." This unit can translate what is said during the meeting in real time and provide it to participants. For example, if a meeting is conducted in English but there are Japanese-speaking participants, the translation unit can translate the English statements into Japanese and display them to the participants. The translation unit can also create meeting minutes in multiple languages ​​and provide them to participants. For example, after the meeting, minutes can be automatically generated in languages ​​such as English, Japanese, and French and distributed to participants. Furthermore, the translation unit can also translate chat messages during the meeting in real time and display them to participants. This facilitates smoother communication between participants who speak different languages ​​and improves the productivity of international meetings.

[0111] The meeting participation system can also include a "feedback section." This section can collect and analyze feedback from participants after the meeting. For example, it can automatically send out questionnaires after the meeting to gather participant satisfaction levels and areas for improvement. Furthermore, the feedback section can analyze the collected feedback and propose improvements for future meetings. For instance, it can suggest improvements to meeting procedures and agenda setting based on participant feedback. Additionally, the feedback section can share the feedback results with participants, increasing transparency. This ultimately improves the quality of meetings and increases participant satisfaction.

[0112] The meeting participation system can also be equipped with an "emotion analysis unit." This unit can analyze participants' emotions in real time during a meeting and use the findings to improve the meeting's progress. For example, it can analyze participants' facial expressions and voices to estimate emotions such as stress and excitement. Furthermore, the emotion analysis unit can adjust the meeting's progress based on the estimated emotions. For instance, if a participant is feeling stressed, the meeting can be temporarily suspended to provide a more relaxed environment. Additionally, the emotion analysis unit can provide a report on emotional changes after the meeting, which can be used to improve future meetings. This enables meeting management that considers participants' emotions, thereby increasing meeting productivity.

[0113] The meeting participation system can also include a "health management department." This department can monitor participants' health during the meeting and provide appropriate advice. For example, it can measure participants' heart rate and stress levels, monitoring their health in real time. The health management department can also display notifications encouraging participants to stretch or take breaks periodically to prevent prolonged sitting. Furthermore, the health management department can provide a health report after the meeting to aid in participant health management. This enables efficient meeting management while maintaining participants' health.

[0114] The meeting participation system can also include a "networking section." This section can provide functions to facilitate interaction among meeting participants. For example, it can share participant profiles before the meeting and match participants with shared interests. It can also automatically set up breakout sessions during the meeting to promote smaller group discussions. Furthermore, it can provide a function to exchange contact information among participants after the meeting, supporting continued interaction. This enhances networking among participants and increases the value of the meeting.

[0115] The meeting participation system can also be equipped with an "emotion estimation unit." This unit can estimate the emotions of participants in a meeting in real time and use this information to improve the meeting's progress. For example, it can analyze participants' facial expressions and voices to estimate emotions such as stress and excitement. The emotion estimation unit can also adjust the meeting's progress based on the estimated emotions. For instance, if a participant is feeling stressed, the meeting can be temporarily suspended to provide a more relaxed environment. Furthermore, the emotion estimation unit can provide a report on emotional changes after the meeting, which can be used to improve future meetings. This enables meeting management that takes participants' emotions into consideration, thereby improving meeting productivity.

[0116] The meeting participation system can also be equipped with an "emotional feedback unit." This unit can collect and analyze feedback based on participants' emotions after the meeting. For example, it can automatically send out questionnaires after the meeting to collect participants' emotions and satisfaction levels. The emotional feedback unit can also analyze the collected emotional data and suggest improvements for the next meeting. For example, it can suggest improvements to the meeting's flow and agenda setting based on participants' emotional data. Furthermore, the emotional feedback unit can share the feedback results with participants, increasing transparency. This improves the quality of the meeting and increases participant satisfaction.

[0117] The meeting participation system can also be equipped with an "emotion notification unit." This unit can estimate the emotions of participants in a meeting in real time and display notifications at the appropriate time. For example, if a participant is feeling stressed, it can notify them with advice to help them relax. If a participant is relaxed, the unit can notify them with advice to help them concentrate. Furthermore, after the meeting, the unit can provide a report on the changes in emotions, which can be used to improve future meetings. This enables meeting management that takes participants' emotions into consideration, thereby improving meeting productivity.

[0118] The meeting participation system can also be equipped with an "emotional support unit." This unit can estimate participants' emotions in real time during a meeting and provide appropriate support. For example, if a participant is feeling tense, it can play relaxing music. It can also provide a relaxing environment if a participant is feeling stressed. Furthermore, after the meeting, the unit can provide a report on emotional changes, which can be used to improve future meetings. This enables meeting management that takes participants' emotions into consideration, thereby improving meeting productivity.

[0119] The meeting participation system can also be equipped with an "emotional monitoring unit." This unit can monitor participants' emotions in real time during a meeting and take appropriate action. For example, if a participant is feeling stressed, the meeting can be temporarily suspended to provide a more relaxed environment. If a participant is relaxed, the unit can offer advice to improve their concentration. Furthermore, after the meeting, the unit can provide a report on emotional changes, which can be used to improve future meetings. This enables meeting management that considers participants' emotions, thereby increasing meeting productivity.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The data collection unit synchronizes with the user's calendar. The data collection unit can synchronize with, for example, a calendar. The data collection unit can select the appropriate synchronization method depending on the type of calendar. Step 2: The notification unit displays a notification based on the meeting start time obtained by the collection unit. The notification unit can display notifications in various ways, such as pop-up notifications, email notifications, and push notifications. As the meeting start time approaches, the notification unit can display reminders 10 minutes, 5 minutes, and 1 minute beforehand. For example, a pop-up notification will appear on the user's desktop screen. An email notification will be sent to the user's email address. A push notification will appear on the user's smartphone. Step 3: Participants automatically join the meeting based on reminders displayed by the notification unit. Participants can, for example, automatically launch the meeting tool and join the meeting without clicking a meeting link. Participants can automatically launch the meeting tool, log in, and configure camera and microphone settings. For example, a participant can obtain a meeting link and automatically launch the meeting tool. Once the meeting tool is launched, the participant can automatically configure the user's camera and microphone.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the collection unit, notification unit, participation unit, preparation unit, productivity improvement unit, and attendance rate improvement unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit synchronizes with the calendar application of the smart device 14 and acquires calendar information by the specific processing unit 290 of the data processing unit 12. The notification unit displays pop-up notifications and push notifications by the control unit 46A of the smart device 14 and sends email notifications by the specific processing unit 290 of the data processing unit 12. The participation unit automatically launches the meeting tool by the control unit 46A of the smart device 14 and acquires the meeting link by the specific processing unit 290 of the data processing unit 12. The preparation unit automatically configures the camera and microphone settings by the control unit 46A of the smart device 14 and logs into the meeting tool by the specific processing unit 290 of the data processing unit 12. The productivity improvement unit shares the agenda in advance by the control unit 46A of the smart device 14 and manages timekeeping by the specific processing unit 290 of the data processing unit 12. The attendance rate improvement unit displays a reminder via the control unit 46A of the smart device 14 and provides an automatic participation function via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the collection unit, notification unit, participation unit, preparation unit, productivity improvement unit, and attendance rate improvement unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit synchronizes with the calendar application of the smart glasses 214 and acquires calendar information by the specific processing unit 290 of the data processing unit 12. The notification unit displays pop-up notifications and push notifications by the control unit 46A of the smart glasses 214 and sends email notifications by the specific processing unit 290 of the data processing unit 12. The participation unit automatically launches the meeting tool by the control unit 46A of the smart glasses 214 and acquires the meeting link by the specific processing unit 290 of the data processing unit 12. The preparation unit automatically configures the camera and microphone settings by the control unit 46A of the smart glasses 214 and logs into the meeting tool by the specific processing unit 290 of the data processing unit 12. The productivity improvement unit shares the agenda in advance by the control unit 46A of the smart glasses 214 and manages timekeeping by the specific processing unit 290 of the data processing unit 12. The attendance rate improvement unit displays a reminder via the control unit 46A of the smart glasses 214 and provides an automatic participation function via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, notification unit, participation unit, preparation unit, productivity improvement unit, and attendance rate improvement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit synchronizes with the calendar application of the headset terminal 314 and acquires calendar information by the specific processing unit 290 of the data processing unit 12. The notification unit displays pop-up notifications and push notifications by the control unit 46A of the headset terminal 314 and sends email notifications by the specific processing unit 290 of the data processing unit 12. The participation unit automatically starts the meeting tool by the control unit 46A of the headset terminal 314 and acquires the meeting link by the specific processing unit 290 of the data processing unit 12. The preparation unit automatically sets up the camera and microphone by the control unit 46A of the headset terminal 314 and logs into the meeting tool by the specific processing unit 290 of the data processing unit 12. The productivity improvement unit shares the agenda in advance by the control unit 46A of the headset terminal 314 and manages timekeeping by the specific processing unit 290 of the data processing unit 12. The attendance rate improvement unit displays a reminder via the control unit 46A of the headset terminal 314 and provides an automatic participation function via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the collection unit, notification unit, participation unit, preparation unit, productivity improvement unit, and attendance rate improvement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit synchronizes with the robot 414's calendar application and acquires calendar information by the specific processing unit 290 of the data processing unit 12. The notification unit displays pop-up notifications and push notifications by the control unit 46A of the robot 414 and sends email notifications by the specific processing unit 290 of the data processing unit 12. The participation unit automatically starts the meeting tool by the control unit 46A of the robot 414 and acquires the meeting link by the specific processing unit 290 of the data processing unit 12. The preparation unit automatically sets up the camera and microphone by the control unit 46A of the robot 414 and logs into the meeting tool by the specific processing unit 290 of the data processing unit 12. The productivity improvement unit shares the agenda in advance by the control unit 46A of the robot 414 and manages timekeeping by the specific processing unit 290 of the data processing unit 12. The attendance rate improvement unit displays a reminder via the control unit 46A of the robot 414 and provides an automatic participation function via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A data collection unit that synchronizes with the user's calendar, A notification unit that displays a notification based on the meeting start time acquired by the collection unit, The system includes a participant unit that automatically joins a meeting based on a reminder displayed by the notification unit. A system characterized by the following features. (Note 2) A preparation department is provided to reduce meeting preparation time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned preparation unit is Automate preparatory tasks such as checking meeting links, logging into meeting tools, and setting up cameras and microphones. The system described in Appendix 2, characterized by the features described herein. (Note 4) It is equipped with a productivity improvement unit to enhance the productivity of meetings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The productivity improvement unit is, By avoiding the time spent manually searching for meeting links and technical problems, the time from the start of the meeting to getting down to business is reduced. The system described in Appendix 4, characterized by the features described herein. (Note 6) It is equipped with an attendance rate improvement unit to improve attendance and participation rates. The system described in Appendix 1, characterized by the features described herein. (Note 7) The attendance rate improvement unit is, Reminders and auto-joining features reduce the likelihood of forgetting to attend or being late. The system described in Appendix 6, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the calendar synchronization timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past meeting participation history and select the optimal synchronization method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When syncing the calendar, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of calendar events to synchronize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When syncing the calendar, the system prioritizes syncing highly relevant events by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When syncing the calendar, the system analyzes the user's social media activity and syncs relevant events. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the meeting. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the meeting category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending notifications, prioritize them based on when the meeting will start. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the meetings. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned participating section is, It estimates the user's emotions and adjusts the automated participation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned participating section is, When automatically joining, the system analyzes the user's past participation history to select the optimal participation method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned participating section is, When automatically joining, the method of participation is customized based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned participating section is, It estimates the user's emotions and determines the priority of automated participation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned participating section is, When automatically joining, the system selects the optimal participation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned participating section is, When users automatically join, their social media activity is analyzed to suggest ways to participate. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned preparation unit is We estimate the user's emotions and adjust the preparation process based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned preparation unit is During the preparation process, the system analyzes the user's past preparation history to select the optimal preparation method. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned preparation unit is The system estimates the user's emotions and prioritizes preparation tasks based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned preparation unit is During the preparation process, the optimal preparation method is selected, taking into account the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 30) The productivity improvement unit is, It estimates user emotions and adjusts productivity improvement methods based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The productivity improvement unit is, When improving productivity, the system analyzes the user's past meeting history to select the most suitable productivity improvement method. The system described in Appendix 3, characterized by the features described herein. (Note 32) The productivity improvement unit is, It estimates user emotions and prioritizes productivity improvements based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The productivity improvement unit is, When improving productivity, the optimal productivity improvement method is selected by considering the user's device information. The system described in Appendix 3, characterized by the features described herein. (Note 34) The attendance rate improvement unit is, The system estimates user sentiment and adjusts methods for improving attendance rates based on the estimated sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 35) The attendance rate improvement unit is, When improving attendance rates, the system analyzes users' past attendance history to select the most effective method for increasing attendance. The system described in Appendix 6, characterized by the features described herein. (Note 36) The attendance rate improvement unit is, The system estimates user sentiment and prioritizes attendance improvement based on the estimated user sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 37) The attendance rate improvement unit is, When improving attendance rates, the optimal method for improving attendance rates is selected by considering the user's device information. The system described in Appendix 6, characterized by the features described herein. (Note 38) The attendance rate improvement unit is, When improving attendance rates, the system selects the most suitable method for improving attendance rates by referring to the user's calendar information. The system described in Appendix 6, characterized by the features described herein. (Note 39) The attendance rate improvement unit is, When improving attendance rates, analyze users' social media activity to select the most effective method for increasing attendance. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that synchronizes with the user's calendar, A notification unit that displays a notification based on the meeting start time acquired by the collection unit, The system includes a participant unit that automatically joins a meeting based on a reminder displayed by the notification unit. A system characterized by the following features.

2. A preparation department is provided to reduce meeting preparation time. The system according to feature 1.

3. The aforementioned preparation unit is Automate preparatory tasks such as checking meeting links, logging into meeting tools, and setting up cameras and microphones. The system according to feature 2.

4. It is equipped with a productivity improvement unit to enhance the productivity of meetings. The system according to feature 1.

5. The productivity improvement unit is, By avoiding the time spent manually searching for meeting links and technical problems, the time from the start of the meeting to getting down to business is reduced. The system according to feature 4.

6. It is equipped with an attendance rate improvement unit to improve attendance and participation rates. The system according to feature 1.

7. The attendance rate improvement unit is, Reminders and auto-joining features reduce the likelihood of forgetting to attend or being late. The system described in claim 6.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the calendar synchronization timing based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past meeting participation history and select the optimal synchronization method. The system according to feature 1.

10. The aforementioned collection unit is When syncing the calendar, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

Images